Unstructured and structured data segments, including documents, may contain numerical, text, speech, music, image, video, and other types of data. These data segments, including documents, may contain information which may be useful but difficult for humans and/or computer analytics systems and software to comprehend on direct access. More and more individuals and organizations attempt to extract relevant knowledge and various insights from various sources of data to assist activities they engage in (such as organizational functions), without creating additional burden for themselves or their employees. While knowledge is often a subjective concept on the outset, the value associated with this concept is widely accepted. In some contexts, knowledge may refer to possession of a deep understanding of the inner workings and functioning of activities, steps, components, and temporal and other relationships, (such as social, matrix, and tensor related), within a specific domain, or even more broadly, across domains. Knowledge that has value to a specific individual, user, or organization may include elements that are not commonly available or understood, those that are refined, structured and granulated, and those that are frequently used in several individual or organizational activities.
Humans gain knowledge in natural ways but this knowledge is not necessarily represented in a form that may be reused by machines. This human acquired knowledge may either never be documented or documented in unstructured text or various forms such as data usage patterns for voice, numerical data, text and SMS, images and photographs, video, music, and the like. Reviewing such a compilation and understanding the exact intent behind this data may require interpretation of the language and/or context. A service delivery organization, for example, may use knowledge to address a specific requirement or to solve a specific problem. Of course, data is increasingly being generated by machines, and in immense volumes. For humans, this volume of data is often overwhelming. For instance, marketing personnel are often unable to comprehend the metaknowledge process patterns latent in the data usage patterns of mobile phones (and other similar smart mobile devices). The data in these environments can be available at a greatly detailed level, by user program member, device, application, etc. These meta-knowledge level process extraction tasks can be challenging even for humans that generated the data, let alone machines. Machines can rapidly explore an immense number of possibilities and patterns. Humans, however, have the ability to comprehend the intent and the circumstances under which such knowledge is applicable, which is helpful in creating or updating this knowledge. Machines, on the other hand, are much more constrained in their ability to understand unstructured text or other business patterns such as data usage pattern in a social group, or network, in a similar context. Nevertheless, knowledge extraction and reuse based on human resources alone is not very scalable within organizations, in which ever-increasing products and associated documentation result in an exponential growth of unstructured data, including numerical data, text, image, video, speech, music, etc. Mechanisms to automate or semi-automate the knowledge extraction and reuse process may be needed to assist expensive human resources in achieving enhanced effectiveness and productivity by having machines assist in optimizing human-machine interactions. Conversely, humans may have a role to play in machines identifying meta knowledge processes in real time.